code: def test_torch_lstm(
      *,
      dtypes_kwargs,
      on_device,
      fn_tree,
      frontend,
      test_flags,
      backend_fw,
  ):
      dtypes, kwargs = dtypes_kwargs
      # Todo: Debug the function to have this case passing as well
      assume("batch_sizes" not in kwargs)
      helpers.test_frontend_function(
          input_dtypes=dtypes,
          backend_to_test=backend_fw,
          frontend=frontend,
          test_flags=test_flags,
          fn_tree=fn_tree,
          on_device=on_device,
          **kwargs,
      )
  
file_path: ivy/ivy_tests/test_ivy/test_frontends/test_torch/test_nn/test_functional/test_layer_functions.py
imports: from hypothesis import assume, strategies
  import numpy
  import ivy
  from ivy.functional.ivy.layers import _get_embed_dim, _pack_padded_sequence
  from ivy_tests.test_ivy import helpers
  from ivy_tests.test_ivy.helpers import handle_frontend_test
  from ivy_tests.test_ivy.test_functional.test_nn.test_layers import _mha_helper
label_desc: Use image node_id 2 for calling a global function with example usage: test_torch_lstm() without return types